Improving the ambiguity resolution with the consideration of unmodeled errors in GNSS medium and long baselines

Abstract Ambiguity resolution (AR) is fundamental to achieve high-precision solution in GNSS (Global Navigation Satellite System) relative positioning. Extensive research has shown that systematic errors are associated with the performance of AR. However, due to the physical complexity, some systematic errors would inevitably remain in the observation equations even after processed with some popular models and parameterization. In the medium and long baselines, these unmodeled errors are the leading cause of the slow or even incorrect fixation of ambiguity. Therefore, to improve the AR performance in the medium and long baselines, we present a procedure with the careful consideration of unmodeled errors. At first, we develop a method to estimate the unmodeled errors based on the float ambiguity bias. Then, the overall procedure and key steps to fix the float solutions corrected by the unmodeled error estimate is designed. Finally, some real-measured baselines (from 68 km to 120 km) are utilized to validate the proposed procedure. The experimental results are analyzed and discussed from the aspects of AR and positioning, respectively. For the AR performance, the time required for the first fixation have been reduced by about 41.58% to 83.51%, from 12 to 100 min. Besides, 12.72% to 48.59% and 2.96% to 36.28% improvements of the ambiguity-fixed rate and the ambiguity-correct rate can be respectively obtained in the four baselines. As for the positioning performance, the mean values and RMSEs have improved by 0.2 to 4.8 cm (1.63% to 22.43%) and 0.2 to 2.8 cm (1.47% to 10.57%), respectively.